Accelerate the Deployment of Secure and Compliant Modern Data Architectures for Advanced Analytics and AI - Modern Data Architecture Accelerator

Accelerate the Deployment of Secure and Compliant Modern Data Architectures for Advanced Analytics and AI

Publication date: August 2025. For updates, refer to CHANGELOG.md file in the MDAA Developer Guide.

The Modern Data Architecture Accelerator (MDAA) on AWS helps customers rapidly deploy and manage sophisticated data platform architectures on AWS. This solution provides a flexible framework that can adapt to most common analytics platform architectures, including basic Data Lakes and Data Warehouses, Lake House architectures, complex Data Mesh implementations, and generative AI development environments. The solution helps you establish a modern data foundation with built-in security, governance, and operational capabilities. Through a simplified configuration approach, you can:

  • Deploy generative AI development environments with integrated AWS services

  • Configure and manage AI/ML workloads including generative AI solutions

  • Deploy data environments across multiple domains and AWS accounts

  • Implement AWS reference architectures like Modern Data Architecture (Lake House)

  • Configure and manage data mesh nodes for distributed data platforms

  • Manage data governance controls and security services

  • Define and deploy purpose-built analytics services for specific use cases

  • Deploy machine learning workload environments

  • Customise deployments through infrastructure-as-code using AWS CDK

  • Configure data ingestion patterns, storage layers, and processing capabilities

MDAA is provided as an open-source solution that can be deployed from locally cloned source code or published NPM packages. You pay only for AWS services enabled to set up your data platform and operate your workloads.

Key Benefits

  • Accelerated Time to Value: Deploy a production-ready modern data platform in weeks instead of months

  • Built-in Data Governance: Implement data security, privacy, and compliance controls from day one

  • Standardised Architecture: Ensure consistent data handling patterns and practices across the organisation

  • Analytics-Ready Infrastructure: Pre-configured analytics services and data processing pipelines, including integrated components for developing and deploying generative AI solutions

  • Cost Optimisation: Built-in cost management and data lifecycle optimisation features

The intended audience for using this solution’s features and capabilities in their environment are data platform engineers, data architects, analytics teams and cloud operations professionals.

Use this navigation table to quickly find answers to these questions:

This implementation guide describes architectural considerations and configuration steps for deploying MDAA. It includes links to AWS CloudFormation templates synthesised from AWS CDK that launch and configure the AWS services required to deploy this solution using AWS best practices for security and availability

If you want to . . . Read . . .

Know the cost for running this solution.

The cost will depend on the modules and other custom features you want to deploy.

Cost

Know which AWS Regions support this solution.

Supported AWS Regions

Access the source code.

GitHub repository